Overview

Dataset statistics

Number of variables21
Number of observations2000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory328.2 KiB
Average record size in memory168.1 B

Variable types

Numeric14
Categorical7

Alerts

fc is highly overall correlated with pcHigh correlation
pc is highly overall correlated with fcHigh correlation
ram is highly overall correlated with price_rangeHigh correlation
four_g is highly overall correlated with three_gHigh correlation
three_g is highly overall correlated with four_gHigh correlation
price_range is highly overall correlated with ramHigh correlation
price_range is uniformly distributedUniform
fc has 474 (23.7%) zerosZeros
pc has 101 (5.1%) zerosZeros
sc_w has 180 (9.0%) zerosZeros

Reproduction

Analysis started2023-03-29 14:45:39.390206
Analysis finished2023-03-29 14:46:26.234129
Duration46.84 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

battery_power
Real number (ℝ)

Distinct1094
Distinct (%)54.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1238.5185
Minimum501
Maximum1998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2023-03-29T19:16:26.439583image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum501
5-th percentile570.95
Q1851.75
median1226
Q31615.25
95-th percentile1930.15
Maximum1998
Range1497
Interquartile range (IQR)763.5

Descriptive statistics

Standard deviation439.41821
Coefficient of variation (CV)0.35479341
Kurtosis-1.2241439
Mean1238.5185
Median Absolute Deviation (MAD)382
Skewness0.031898472
Sum2477037
Variance193088.36
MonotonicityNot monotonic
2023-03-29T19:16:26.633600image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1872 6
 
0.3%
618 6
 
0.3%
1589 6
 
0.3%
1715 5
 
0.2%
1807 5
 
0.2%
1310 5
 
0.2%
1083 5
 
0.2%
1512 5
 
0.2%
1379 5
 
0.2%
1949 5
 
0.2%
Other values (1084) 1947
97.4%
ValueCountFrequency (%)
501 2
 
0.1%
502 2
 
0.1%
503 3
0.1%
504 5
0.2%
506 1
 
0.1%
507 2
 
0.1%
508 3
0.1%
509 1
 
0.1%
510 3
0.1%
511 4
0.2%
ValueCountFrequency (%)
1998 1
 
0.1%
1997 1
 
0.1%
1996 2
0.1%
1995 2
0.1%
1994 3
0.1%
1993 1
 
0.1%
1992 2
0.1%
1991 4
0.2%
1989 2
0.1%
1988 1
 
0.1%

blue
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
0
1010 
1
990 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 1010
50.5%
1 990
49.5%

Length

2023-03-29T19:16:26.855010image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-29T19:16:27.019920image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 1010
50.5%
1 990
49.5%

Most occurring characters

ValueCountFrequency (%)
0 1010
50.5%
1 990
49.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1010
50.5%
1 990
49.5%

Most occurring scripts

ValueCountFrequency (%)
Common 2000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1010
50.5%
1 990
49.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1010
50.5%
1 990
49.5%

clock_speed
Real number (ℝ)

Distinct26
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.52225
Minimum0.5
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2023-03-29T19:16:27.157041image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile0.5
Q10.7
median1.5
Q32.2
95-th percentile2.8
Maximum3
Range2.5
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation0.81600421
Coefficient of variation (CV)0.53605138
Kurtosis-1.3234172
Mean1.52225
Median Absolute Deviation (MAD)0.8
Skewness0.17808412
Sum3044.5
Variance0.66586287
MonotonicityNot monotonic
2023-03-29T19:16:27.353515image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0.5 413
20.6%
2.8 85
 
4.2%
2.3 78
 
3.9%
2.1 76
 
3.8%
1.6 76
 
3.8%
2.5 74
 
3.7%
0.6 74
 
3.7%
1.4 70
 
3.5%
1.3 68
 
3.4%
1.5 67
 
3.4%
Other values (16) 919
46.0%
ValueCountFrequency (%)
0.5 413
20.6%
0.6 74
 
3.7%
0.7 64
 
3.2%
0.8 58
 
2.9%
0.9 58
 
2.9%
1 61
 
3.0%
1.1 51
 
2.5%
1.2 56
 
2.8%
1.3 68
 
3.4%
1.4 70
 
3.5%
ValueCountFrequency (%)
3 28
 
1.4%
2.9 62
3.1%
2.8 85
4.2%
2.7 55
2.8%
2.6 55
2.8%
2.5 74
3.7%
2.4 58
2.9%
2.3 78
3.9%
2.2 59
2.9%
2.1 76
3.8%

dual_sim
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
1
1019 
0
981 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 1019
50.9%
0 981
49.0%

Length

2023-03-29T19:16:27.520541image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-29T19:16:27.666151image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1 1019
50.9%
0 981
49.0%

Most occurring characters

ValueCountFrequency (%)
1 1019
50.9%
0 981
49.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1019
50.9%
0 981
49.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1019
50.9%
0 981
49.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1019
50.9%
0 981
49.0%

fc
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3095
Minimum0
Maximum19
Zeros474
Zeros (%)23.7%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2023-03-29T19:16:27.796306image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q37
95-th percentile13
Maximum19
Range19
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.3414437
Coefficient of variation (CV)1.0074124
Kurtosis0.27707632
Mean4.3095
Median Absolute Deviation (MAD)3
Skewness1.0198114
Sum8619
Variance18.848134
MonotonicityNot monotonic
2023-03-29T19:16:27.964855image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 474
23.7%
1 245
12.2%
2 189
 
9.4%
3 170
 
8.5%
5 139
 
7.0%
4 133
 
6.7%
6 112
 
5.6%
7 100
 
5.0%
9 78
 
3.9%
8 77
 
3.9%
Other values (10) 283
14.1%
ValueCountFrequency (%)
0 474
23.7%
1 245
12.2%
2 189
 
9.4%
3 170
 
8.5%
4 133
 
6.7%
5 139
 
7.0%
6 112
 
5.6%
7 100
 
5.0%
8 77
 
3.9%
9 78
 
3.9%
ValueCountFrequency (%)
19 1
 
0.1%
18 11
 
0.5%
17 6
 
0.3%
16 24
 
1.2%
15 23
 
1.1%
14 20
 
1.0%
13 40
2.0%
12 45
2.2%
11 51
2.5%
10 62
3.1%

four_g
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
1
1043 
0
957 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 1043
52.1%
0 957
47.9%

Length

2023-03-29T19:16:28.145585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-29T19:16:28.291238image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1 1043
52.1%
0 957
47.9%

Most occurring characters

ValueCountFrequency (%)
1 1043
52.1%
0 957
47.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1043
52.1%
0 957
47.9%

Most occurring scripts

ValueCountFrequency (%)
Common 2000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1043
52.1%
0 957
47.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1043
52.1%
0 957
47.9%

int_memory
Real number (ℝ)

Distinct63
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.0465
Minimum2
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2023-03-29T19:16:28.451693image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q116
median32
Q348
95-th percentile61
Maximum64
Range62
Interquartile range (IQR)32

Descriptive statistics

Standard deviation18.145715
Coefficient of variation (CV)0.56623079
Kurtosis-1.216074
Mean32.0465
Median Absolute Deviation (MAD)16
Skewness0.057889328
Sum64093
Variance329.26697
MonotonicityNot monotonic
2023-03-29T19:16:28.659138image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27 47
 
2.4%
16 45
 
2.2%
14 45
 
2.2%
57 42
 
2.1%
2 42
 
2.1%
42 40
 
2.0%
7 40
 
2.0%
44 39
 
1.9%
30 39
 
1.9%
6 37
 
1.8%
Other values (53) 1584
79.2%
ValueCountFrequency (%)
2 42
2.1%
3 25
1.2%
4 20
1.0%
5 36
1.8%
6 37
1.8%
7 40
2.0%
8 37
1.8%
9 35
1.8%
10 36
1.8%
11 34
1.7%
ValueCountFrequency (%)
64 31
1.6%
63 30
1.5%
62 21
1.1%
61 27
1.4%
60 27
1.4%
59 18
0.9%
58 36
1.8%
57 42
2.1%
56 27
1.4%
55 29
1.5%

m_dep
Real number (ℝ)

Distinct10
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.50175
Minimum0.1
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2023-03-29T19:16:28.842648image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.1
Q10.2
median0.5
Q30.8
95-th percentile1
Maximum1
Range0.9
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation0.28841555
Coefficient of variation (CV)0.57481923
Kurtosis-1.2743489
Mean0.50175
Median Absolute Deviation (MAD)0.3
Skewness0.08908201
Sum1003.5
Variance0.083183529
MonotonicityNot monotonic
2023-03-29T19:16:28.972342image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0.1 320
16.0%
0.2 213
10.7%
0.8 208
10.4%
0.5 205
10.2%
0.7 200
10.0%
0.3 199
10.0%
0.9 195
9.8%
0.6 186
9.3%
0.4 168
8.4%
1 106
 
5.3%
ValueCountFrequency (%)
0.1 320
16.0%
0.2 213
10.7%
0.3 199
10.0%
0.4 168
8.4%
0.5 205
10.2%
0.6 186
9.3%
0.7 200
10.0%
0.8 208
10.4%
0.9 195
9.8%
1 106
 
5.3%
ValueCountFrequency (%)
1 106
 
5.3%
0.9 195
9.8%
0.8 208
10.4%
0.7 200
10.0%
0.6 186
9.3%
0.5 205
10.2%
0.4 168
8.4%
0.3 199
10.0%
0.2 213
10.7%
0.1 320
16.0%

mobile_wt
Real number (ℝ)

Distinct121
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean140.249
Minimum80
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2023-03-29T19:16:29.139698image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum80
5-th percentile86
Q1109
median141
Q3170
95-th percentile196
Maximum200
Range120
Interquartile range (IQR)61

Descriptive statistics

Standard deviation35.399655
Coefficient of variation (CV)0.25240576
Kurtosis-1.2103765
Mean140.249
Median Absolute Deviation (MAD)31
Skewness0.0065581574
Sum280498
Variance1253.1356
MonotonicityNot monotonic
2023-03-29T19:16:29.349004image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
182 28
 
1.4%
101 27
 
1.4%
185 27
 
1.4%
146 26
 
1.3%
199 26
 
1.3%
88 25
 
1.2%
198 25
 
1.2%
105 25
 
1.2%
89 24
 
1.2%
131 23
 
1.1%
Other values (111) 1744
87.2%
ValueCountFrequency (%)
80 21
1.1%
81 13
0.7%
82 15
0.8%
83 19
0.9%
84 17
0.9%
85 13
0.7%
86 19
0.9%
87 15
0.8%
88 25
1.2%
89 24
1.2%
ValueCountFrequency (%)
200 19
0.9%
199 26
1.3%
198 25
1.2%
197 19
0.9%
196 20
1.0%
195 11
0.5%
194 16
0.8%
193 15
0.8%
192 15
0.8%
191 15
0.8%

n_cores
Real number (ℝ)

Distinct8
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5205
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2023-03-29T19:16:29.549464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q37
95-th percentile8
Maximum8
Range7
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.2878367
Coefficient of variation (CV)0.50610258
Kurtosis-1.2297498
Mean4.5205
Median Absolute Deviation (MAD)2
Skewness0.0036275083
Sum9041
Variance5.2341968
MonotonicityNot monotonic
2023-03-29T19:16:29.702059image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
4 274
13.7%
7 259
13.0%
8 256
12.8%
2 247
12.3%
3 246
12.3%
5 246
12.3%
1 242
12.1%
6 230
11.5%
ValueCountFrequency (%)
1 242
12.1%
2 247
12.3%
3 246
12.3%
4 274
13.7%
5 246
12.3%
6 230
11.5%
7 259
13.0%
8 256
12.8%
ValueCountFrequency (%)
8 256
12.8%
7 259
13.0%
6 230
11.5%
5 246
12.3%
4 274
13.7%
3 246
12.3%
2 247
12.3%
1 242
12.1%

pc
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9165
Minimum0
Maximum20
Zeros101
Zeros (%)5.1%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2023-03-29T19:16:29.869608image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median10
Q315
95-th percentile20
Maximum20
Range20
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.0643149
Coefficient of variation (CV)0.61153784
Kurtosis-1.1714988
Mean9.9165
Median Absolute Deviation (MAD)5
Skewness0.01730615
Sum19833
Variance36.775916
MonotonicityNot monotonic
2023-03-29T19:16:30.069073image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
10 122
 
6.1%
7 119
 
5.9%
9 112
 
5.6%
20 110
 
5.5%
1 104
 
5.2%
14 104
 
5.2%
0 101
 
5.1%
2 99
 
5.0%
17 99
 
5.0%
6 95
 
4.8%
Other values (11) 935
46.8%
ValueCountFrequency (%)
0 101
5.1%
1 104
5.2%
2 99
5.0%
3 93
4.7%
4 95
4.8%
5 59
2.9%
6 95
4.8%
7 119
5.9%
8 89
4.5%
9 112
5.6%
ValueCountFrequency (%)
20 110
5.5%
19 83
4.2%
18 82
4.1%
17 99
5.0%
16 88
4.4%
15 92
4.6%
14 104
5.2%
13 85
4.2%
12 90
4.5%
11 79
4.0%

px_height
Real number (ℝ)

Distinct1137
Distinct (%)56.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean645.108
Minimum0
Maximum1960
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2023-03-29T19:16:30.269537image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile70.95
Q1282.75
median564
Q3947.25
95-th percentile1485.05
Maximum1960
Range1960
Interquartile range (IQR)664.5

Descriptive statistics

Standard deviation443.78081
Coefficient of variation (CV)0.68791708
Kurtosis-0.31586549
Mean645.108
Median Absolute Deviation (MAD)318
Skewness0.66627126
Sum1290216
Variance196941.41
MonotonicityNot monotonic
2023-03-29T19:16:30.499923image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
347 7
 
0.4%
179 6
 
0.3%
371 6
 
0.3%
275 6
 
0.3%
674 5
 
0.2%
286 5
 
0.2%
42 5
 
0.2%
211 5
 
0.2%
649 5
 
0.2%
398 5
 
0.2%
Other values (1127) 1945
97.2%
ValueCountFrequency (%)
0 2
0.1%
1 1
 
0.1%
2 1
 
0.1%
3 2
0.1%
4 3
0.1%
5 1
 
0.1%
6 1
 
0.1%
7 1
 
0.1%
8 2
0.1%
9 1
 
0.1%
ValueCountFrequency (%)
1960 1
0.1%
1949 1
0.1%
1920 1
0.1%
1914 1
0.1%
1901 1
0.1%
1899 1
0.1%
1895 1
0.1%
1878 1
0.1%
1874 1
0.1%
1869 1
0.1%

px_width
Real number (ℝ)

Distinct1109
Distinct (%)55.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1251.5155
Minimum500
Maximum1998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2023-03-29T19:16:30.719342image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile579.85
Q1874.75
median1247
Q31633
95-th percentile1929.05
Maximum1998
Range1498
Interquartile range (IQR)758.25

Descriptive statistics

Standard deviation432.19945
Coefficient of variation (CV)0.34534087
Kurtosis-1.1860052
Mean1251.5155
Median Absolute Deviation (MAD)376
Skewness0.014787474
Sum2503031
Variance186796.36
MonotonicityNot monotonic
2023-03-29T19:16:30.940623image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
874 7
 
0.4%
1247 7
 
0.4%
1383 6
 
0.3%
1463 6
 
0.3%
1469 6
 
0.3%
1393 5
 
0.2%
1781 5
 
0.2%
1767 5
 
0.2%
1923 5
 
0.2%
1429 5
 
0.2%
Other values (1099) 1943
97.2%
ValueCountFrequency (%)
500 2
0.1%
501 2
0.1%
503 1
 
0.1%
506 1
 
0.1%
507 4
0.2%
508 1
 
0.1%
509 2
0.1%
510 3
0.1%
511 2
0.1%
512 2
0.1%
ValueCountFrequency (%)
1998 1
 
0.1%
1997 1
 
0.1%
1996 1
 
0.1%
1995 3
0.1%
1994 2
 
0.1%
1992 1
 
0.1%
1991 1
 
0.1%
1990 1
 
0.1%
1989 3
0.1%
1988 5
0.2%

ram
Real number (ℝ)

Distinct1562
Distinct (%)78.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2124.213
Minimum256
Maximum3998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2023-03-29T19:16:31.172001image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum256
5-th percentile445
Q11207.5
median2146.5
Q33064.5
95-th percentile3826.35
Maximum3998
Range3742
Interquartile range (IQR)1857

Descriptive statistics

Standard deviation1084.732
Coefficient of variation (CV)0.51065126
Kurtosis-1.1919131
Mean2124.213
Median Absolute Deviation (MAD)932.5
Skewness0.0066280354
Sum4248426
Variance1176643.6
MonotonicityNot monotonic
2023-03-29T19:16:31.385053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1464 4
 
0.2%
3142 4
 
0.2%
2610 4
 
0.2%
2227 4
 
0.2%
1229 4
 
0.2%
3654 3
 
0.1%
1277 3
 
0.1%
1050 3
 
0.1%
2775 3
 
0.1%
2674 3
 
0.1%
Other values (1552) 1965
98.2%
ValueCountFrequency (%)
256 1
0.1%
258 2
0.1%
259 1
0.1%
262 1
0.1%
263 1
0.1%
265 1
0.1%
267 1
0.1%
273 1
0.1%
277 1
0.1%
278 2
0.1%
ValueCountFrequency (%)
3998 1
0.1%
3996 1
0.1%
3993 1
0.1%
3991 2
0.1%
3990 1
0.1%
3984 1
0.1%
3978 1
0.1%
3971 1
0.1%
3970 2
0.1%
3969 1
0.1%

sc_h
Real number (ℝ)

Distinct15
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.3065
Minimum5
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2023-03-29T19:16:31.555348image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile6
Q19
median12
Q316
95-th percentile19
Maximum19
Range14
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.213245
Coefficient of variation (CV)0.34235932
Kurtosis-1.1907912
Mean12.3065
Median Absolute Deviation (MAD)4
Skewness-0.098884241
Sum24613
Variance17.751433
MonotonicityNot monotonic
2023-03-29T19:16:31.703951image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
17 193
 
9.7%
12 157
 
7.8%
7 151
 
7.5%
16 143
 
7.1%
14 143
 
7.1%
15 135
 
6.8%
13 131
 
6.6%
11 126
 
6.3%
10 125
 
6.2%
9 124
 
6.2%
Other values (5) 572
28.6%
ValueCountFrequency (%)
5 97
4.9%
6 114
5.7%
7 151
7.5%
8 117
5.9%
9 124
6.2%
10 125
6.2%
11 126
6.3%
12 157
7.8%
13 131
6.6%
14 143
7.1%
ValueCountFrequency (%)
19 124
6.2%
18 120
6.0%
17 193
9.7%
16 143
7.1%
15 135
6.8%
14 143
7.1%
13 131
6.6%
12 157
7.8%
11 126
6.3%
10 125
6.2%

sc_w
Real number (ℝ)

Distinct19
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.767
Minimum0
Maximum18
Zeros180
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2023-03-29T19:16:31.863080image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q39
95-th percentile14
Maximum18
Range18
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.3563976
Coefficient of variation (CV)0.75540101
Kurtosis-0.38952279
Mean5.767
Median Absolute Deviation (MAD)3
Skewness0.63378707
Sum11534
Variance18.9782
MonotonicityNot monotonic
2023-03-29T19:16:32.002584image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1 210
10.5%
3 199
10.0%
4 182
9.1%
0 180
9.0%
5 161
 
8.1%
2 156
 
7.8%
7 132
 
6.6%
6 130
 
6.5%
8 125
 
6.2%
10 107
 
5.3%
Other values (9) 418
20.9%
ValueCountFrequency (%)
0 180
9.0%
1 210
10.5%
2 156
7.8%
3 199
10.0%
4 182
9.1%
5 161
8.1%
6 130
6.5%
7 132
6.6%
8 125
6.2%
9 97
4.9%
ValueCountFrequency (%)
18 8
 
0.4%
17 19
 
0.9%
16 29
 
1.5%
15 31
 
1.6%
14 33
 
1.7%
13 49
2.5%
12 68
3.4%
11 84
4.2%
10 107
5.3%
9 97
4.9%

talk_time
Real number (ℝ)

Distinct19
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.011
Minimum2
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.8 KiB
2023-03-29T19:16:32.157938image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q16
median11
Q316
95-th percentile20
Maximum20
Range18
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.4639552
Coefficient of variation (CV)0.49622697
Kurtosis-1.218591
Mean11.011
Median Absolute Deviation (MAD)5
Skewness0.0095117622
Sum22022
Variance29.854806
MonotonicityNot monotonic
2023-03-29T19:16:32.346441image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
7 124
 
6.2%
4 123
 
6.2%
16 116
 
5.8%
15 115
 
5.8%
19 113
 
5.7%
6 111
 
5.5%
10 105
 
5.2%
8 104
 
5.2%
11 103
 
5.1%
20 102
 
5.1%
Other values (9) 884
44.2%
ValueCountFrequency (%)
2 99
5.0%
3 94
4.7%
4 123
6.2%
5 93
4.7%
6 111
5.5%
7 124
6.2%
8 104
5.2%
9 100
5.0%
10 105
5.2%
11 103
5.1%
ValueCountFrequency (%)
20 102
5.1%
19 113
5.7%
18 100
5.0%
17 98
4.9%
16 116
5.8%
15 115
5.8%
14 101
5.1%
13 100
5.0%
12 99
5.0%
11 103
5.1%

three_g
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
1
1523 
0
477 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1523
76.1%
0 477
 
23.8%

Length

2023-03-29T19:16:32.507010image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-29T19:16:32.654656image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1 1523
76.1%
0 477
 
23.8%

Most occurring characters

ValueCountFrequency (%)
1 1523
76.1%
0 477
 
23.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1523
76.1%
0 477
 
23.8%

Most occurring scripts

ValueCountFrequency (%)
Common 2000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1523
76.1%
0 477
 
23.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1523
76.1%
0 477
 
23.8%

touch_screen
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
1
1006 
0
994 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 1006
50.3%
0 994
49.7%

Length

2023-03-29T19:16:32.788256image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-29T19:16:32.971808image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1 1006
50.3%
0 994
49.7%

Most occurring characters

ValueCountFrequency (%)
1 1006
50.3%
0 994
49.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1006
50.3%
0 994
49.7%

Most occurring scripts

ValueCountFrequency (%)
Common 2000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1006
50.3%
0 994
49.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1006
50.3%
0 994
49.7%

wifi
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
1
1014 
0
986 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 1014
50.7%
0 986
49.3%

Length

2023-03-29T19:16:33.098048image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-29T19:16:33.246659image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1 1014
50.7%
0 986
49.3%

Most occurring characters

ValueCountFrequency (%)
1 1014
50.7%
0 986
49.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1014
50.7%
0 986
49.3%

Most occurring scripts

ValueCountFrequency (%)
Common 2000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1014
50.7%
0 986
49.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1014
50.7%
0 986
49.3%

price_range
Categorical

HIGH CORRELATION  UNIFORM 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
1
500 
2
500 
3
500 
0
500 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 500
25.0%
2 500
25.0%
3 500
25.0%
0 500
25.0%

Length

2023-03-29T19:16:33.376266image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-29T19:16:33.562764image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1 500
25.0%
2 500
25.0%
3 500
25.0%
0 500
25.0%

Most occurring characters

ValueCountFrequency (%)
1 500
25.0%
2 500
25.0%
3 500
25.0%
0 500
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 500
25.0%
2 500
25.0%
3 500
25.0%
0 500
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 500
25.0%
2 500
25.0%
3 500
25.0%
0 500
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 500
25.0%
2 500
25.0%
3 500
25.0%
0 500
25.0%

Interactions

2023-03-29T19:16:22.417115image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:46.640107image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:49.244991image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:51.794596image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:54.414184image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:57.606887image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:00.190793image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:02.845752image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:05.864413image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:08.629471image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:11.325548image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:14.190066image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:17.222956image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:19.788182image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:22.602620image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:46.874762image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:49.422720image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:51.979103image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:54.639556image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:57.781543image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:00.366886image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:03.053195image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:06.092797image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:08.810311image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:11.534987image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:14.416464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:17.396495image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:19.971725image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:22.779148image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:47.052502image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:49.586283image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:52.153637image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:54.802118image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:57.994972image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:00.542458image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:03.266624image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:06.270324image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:09.018450image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:11.709561image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:14.626899image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:17.586984image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:20.144146image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:22.973675image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:47.230245image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:49.766799image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:52.336336image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:55.037488image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:58.169817image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:00.714956image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:03.457115image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:06.470787image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:09.209939image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:11.896302image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:14.864266image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:17.767503image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:20.331643image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:23.186108image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:47.411763image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:49.933990image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:52.515815image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:55.255904image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:58.397228image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:00.887494image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:03.642618image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:06.675241image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:09.388461image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:12.069726image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:15.057745image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:17.939080image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:20.537128image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:23.358562image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:47.578351image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:50.095559image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:52.686398image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:55.460358image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:58.555787image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:01.054722image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:03.829120image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:06.841794image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:09.558046image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:12.256227image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:15.247239image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:18.095820image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:20.704589image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:23.533115image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:47.757835image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:50.272085image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:52.854261image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:55.633894image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:58.739341image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:01.285550image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:04.024601image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:07.043255image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:09.752909image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:12.464667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:15.490587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:18.301271image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:20.880227image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:23.714190image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:47.943750image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:50.445598image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:53.032783image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:55.868268image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:58.948780image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:01.507954image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:04.272934image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:07.255688image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:09.946391image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:12.648220image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:15.748898image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:18.479836image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:21.057752image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:23.899496image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:48.133576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:50.632103image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:53.215957image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:56.123586image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:59.153236image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:01.697447image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:04.494339image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:07.444621image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:10.140875image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:12.830726image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:15.960331image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:18.658319image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:21.282153image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:24.117911image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:48.330439image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:50.808626image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:53.400866image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:56.360948image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:59.338737image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:01.937804image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:04.687824image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:07.670001image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:10.331210image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:13.017190image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:16.164785image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:18.850801image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:21.467656image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:24.346301image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:48.515941image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:50.983511image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:53.581385image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:56.599313image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:59.510290image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:02.115330image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:04.918207image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:07.859494image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:10.558599image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:13.230560image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:16.349292image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:19.052265image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:21.647178image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:24.542777image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:48.694459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:51.273738image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:53.763896image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:56.838673image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:59.682842image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:02.297892image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:05.300206image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:08.041008image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:10.762053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:13.423044image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:16.571698image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:19.227793image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:21.825701image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:24.753249image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:48.874981image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:51.442287image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:53.935436image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:57.007221image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:59.843376image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:02.465768image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:05.474451image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:08.222523image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:10.942576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:13.595583image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:16.750219image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:19.398336image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:22.001229image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:24.967297image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:49.057492image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:51.618621image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:54.170807image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:15:57.241631image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:00.011273image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:02.653265image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:05.681898image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:08.438943image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:11.125086image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:13.774183image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:17.034459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:19.590710image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-29T19:16:22.235601image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-03-29T19:16:33.741289image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
battery_powerclock_speedfcint_memorym_depmobile_wtn_corespcpx_heightpx_widthramsc_hsc_wtalk_timebluedual_simfour_gthree_gtouch_screenwifiprice_range
battery_power1.0000.0090.035-0.0040.0330.002-0.0300.0310.009-0.009-0.001-0.029-0.0270.0530.0340.0530.0000.0000.0000.0000.128
clock_speed0.0091.000-0.0050.005-0.0150.011-0.008-0.006-0.013-0.0090.004-0.030-0.015-0.0130.0500.0220.0460.0370.0340.0000.000
fc0.035-0.0051.000-0.0270.0130.027-0.0160.659-0.021-0.0090.020-0.010-0.001-0.0010.0000.0000.0380.0000.0460.0490.000
int_memory-0.0040.005-0.0271.0000.007-0.034-0.028-0.033-0.002-0.0090.0330.0400.016-0.0020.0570.0000.0000.0280.0220.0100.040
m_dep0.033-0.0150.0130.0071.0000.022-0.0050.0280.0260.023-0.010-0.024-0.0190.0170.0280.0610.0000.0000.0680.0160.017
mobile_wt0.0020.0110.027-0.0340.0221.000-0.0190.0190.0110.001-0.003-0.034-0.0190.0060.0000.0360.0480.0000.0000.0000.027
n_cores-0.030-0.008-0.016-0.028-0.005-0.0191.000-0.002-0.0050.0240.0050.0010.0290.0130.0000.0000.0410.0220.0000.0000.000
pc0.031-0.0060.659-0.0330.0280.019-0.0021.000-0.0150.0030.0290.005-0.0350.0140.0000.0000.0000.0000.0330.0000.029
px_height0.009-0.013-0.021-0.0020.0260.011-0.005-0.0151.0000.468-0.0310.0540.029-0.0100.0000.0000.0210.0250.0000.0630.084
px_width-0.009-0.009-0.009-0.0090.0230.0010.0240.0030.4681.0000.0030.0230.0250.0070.0000.0000.0000.0000.0000.0370.105
ram-0.0010.0040.0200.033-0.010-0.0030.0050.029-0.0310.0031.0000.0160.0260.0120.0000.0230.0070.0430.0000.0000.723
sc_h-0.029-0.030-0.0100.040-0.024-0.0340.0010.0050.0540.0230.0161.0000.470-0.0180.0000.0000.0810.0220.0090.0700.034
sc_w-0.027-0.015-0.0010.016-0.019-0.0190.029-0.0350.0290.0250.0260.4701.000-0.0220.0340.0110.0000.0470.0000.0000.060
talk_time0.053-0.013-0.001-0.0020.0170.0060.0130.014-0.0100.0070.012-0.018-0.0221.0000.0000.0140.0420.0360.0500.0000.000
blue0.0340.0500.0000.0570.0280.0000.0000.0000.0000.0000.0000.0000.0340.0001.0000.0260.0000.0190.0000.0000.000
dual_sim0.0530.0220.0000.0000.0610.0360.0000.0000.0000.0000.0230.0000.0110.0140.0261.0000.0000.0000.0000.0000.000
four_g0.0000.0460.0380.0000.0000.0480.0410.0000.0210.0000.0070.0810.0000.0420.0000.0001.0000.5830.0000.0000.009
three_g0.0000.0370.0000.0280.0000.0000.0220.0000.0250.0000.0430.0220.0470.0360.0190.0000.5831.0000.0000.0000.000
touch_screen0.0000.0340.0460.0220.0680.0000.0000.0330.0000.0000.0000.0090.0000.0500.0000.0000.0000.0001.0000.0000.021
wifi0.0000.0000.0490.0100.0160.0000.0000.0000.0630.0370.0000.0700.0000.0000.0000.0000.0000.0000.0001.0000.000
price_range0.1280.0000.0000.0400.0170.0270.0000.0290.0840.1050.7230.0340.0600.0000.0000.0000.0090.0000.0210.0001.000

Missing values

2023-03-29T19:16:25.561995image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-29T19:16:25.983539image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

battery_powerblueclock_speeddual_simfcfour_gint_memorym_depmobile_wtn_corespcpx_heightpx_widthramsc_hsc_wtalk_timethree_gtouch_screenwifiprice_range
084202.201070.61882220756254997190011
1102110.5101530.7136369051988263117371102
256310.5121410.91455612631716260311291102
361512.5000100.813169121617862769168111002
4182111.20131440.614121412081212141182151101
5185900.5130220.716417100416541067171101001
6182101.7041100.813981038110183220138181013
7195400.5100240.818740512114970016351110
8144510.5000530.71747143868361099171201000
950910.612190.193515113712245131910121000
battery_powerblueclock_speeddual_simfcfour_gint_memorym_depmobile_wtn_corespcpx_heightpx_widthramsc_hsc_wtalk_timethree_gtouch_screenwifiprice_range
1990161712.4081360.8851974314262965371000
1991188202.00111440.811381947433579198201103
199267412.9110210.219834576180911806341110
1993146710.5000180.61225088810993962151151113
199485802.2010500.1841252814163978171631103
199579410.510120.810661412221890668134191100
1996196512.6100390.218743915196520321110161112
1997191100.9111360.710883868163230579151103
1998151200.9041460.1145553366708691810191110
199951012.0151450.9168616483754391919421113